class_docstr: Change the channel number with a one by one convoluational layer.
  
  Args:
      in_channels (int): Number of input channels.
      out_channels (int): Number of output channels.
      init_cfg (dict or list[dict], optional): Initialization configs.
          Defaults to dict(type='Xavier', layer='Conv2d').
code: def __init__(
      self,
      in_channels: int,
      out_channels: int,
      init_cfg: Dict = dict(type="Xavier", layer="Conv2d"),
  ) -> None:
      super().__init__(init_cfg=init_cfg)
  
      self.layer = nn.Conv2d(
          in_channels, out_channels, kernel_size=1, stride=1, padding=0
      )
  
file_path: mmocr/mmocr/models/textrecog/encoders/channel_reduction_encoder.py
imports: from typing import Dict, Optional, Sequence
  import torch
  import torch.nn
  from mmocr.registry import MODELS
  from mmocr.structures import TextRecogDataSample
  from .base import BaseEncoder
label_desc: Use image node_id 1 to create a new ChannelReductionEncoder object from inherited base classes: BaseEncoder with example: obj = ChannelReductionEncoder(in_channels, out_channels, init_cfg)